PRESENTATION TITLE Model-based Dynamic Optimization of the Production of Monoclonal-antibodies in Mammalian Cell Cultures

ABSTRACT Monoclonal antibodies (mAbs) constitute one of the leading products of the biopharmaceutical market with significant therapeutic and diagnostic applications. This has drawn increased attention to the intensification of their production processes, where model-based approaches can be utilized for successful optimization and control purposes. This presentation will discuss how to formulate and solve dynamic optimization problems and its application to different scenarios of the mAb production in mammalian cell cultures. Some results of the model-based optimization will also be shown. These are typically optimal initial nutrient concentrations and/or optimal feeding strategy depending on the operation mode of the bioreactors and the chosen objective functions. Since the models are essential for this approach, the question of developing a model suitable for optimization via several reformulations that are applied to an available predictive energy-based process mode will also be briefly addressed. This presentation will commence with an overview of the AVT – Aachener Verfahrenstechnik (chemical engineering institutes) at RWTH Aachen and especially the research topics at the Process Systems Engineering Group.

ABOUT THE PRESENTER Dr Adel Mhamdi is Senior Lecturer/Researcher and Deputy Director at RWTH Aachen University, Germany. In 2003, he completed his PhD in Process Systems Engineering at RWTH Aachen University. He obtained an MSc in Automatic Control and Signalling Processing in 1993 and an MEng in Electrical Engineering in 1992 from the National Engineering School of Tunis, Tunisia. He has more than 70 publications (book contributions, peer-reviewed papers in international journals, papers in conference proceedings). His research interests include – Model-based experimental analysis: identification of kinetic phenomena; Model-based process control: predictive control (MPC), receding horizon estimation (RHE), dynamic optimization, data reconciliation; Modeling and simulation of challenging processes, e.g. polymerization, desalination; Integration of decentralized control; Formulation and solution of inverse problems (state, parameter and input estimation).